Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

Wheat (Triticum aestivum L.) is a strategic food crop for arid, hot regions such as the Arabian Peninsula, the Middle East, and North Africa. In these areas, production is limited by extreme environmental and agronomic conditions, leading to heavy dependence on imported wheat. Irrigation is often essential for successful cultivation, but available water sources are frequently saline. This study evaluated the comparative effects of irrigation salinity and genotype on agronomic performance, physiological responses, and grain quality. Nine Syrian wheat genotypes and one French bread-making cultivar, Florence Aurora, were grown in sandy soil under three irrigation salinity levels (2.6, 10, and 15 dS m⁻¹) across two seasons at the International Center for Biosaline Agriculture (Dubai, UAE). Salinity strongly negatively impacted yield, which decreased by 61% from the control to 15 dS m⁻¹, along with key yield components such as thousand grain weight and total biomass. Physiological traits, including carbon isotope composition (δ¹³C) and Na concentrations in roots, shoots and grains, increased significantly with salinity, while chlorophyll content showed a modest decline. Effects on grain quality were relatively minor: total nitrogen concentration and most mineral levels increased slightly, mainly due to a passive concentration effect associated with reduced TGW. Genotypes varied significantly in yield, biomass, TGW, physiological traits, and grain quality. The highest-yielding genotypes under control conditions (ACSAD 981 and ACSAD 1147) also performed best under saline conditions, and no trade-off was observed between yield and grain quality parameters (TGW, nitrogen, zinc, and iron concentrations). Separate analyses conducted for control and saline treatments identified different drivers of genotypic variability. Under control conditions, chlorophyll content, closely linked with δ¹³C, was the best predictor of genotypic differences and was positively correlated with yield across genotypes. Under salinity stress, grain magnesium (Mg) concentration was the strongest predictor, followed by grain δ¹³C, with both traits positively correlated with yield. These findings highlight key physiological traits linked to salinity tolerance and offer insights into the mechanisms underlying genotypic variability under both optimal and saline irrigation conditions.

Crops experience different stressors throughout their life cycle. Therefore, and with a view to accelerating genetic progress, it is essential to develop phenotyping protocols under real-world (field) conditions at critical stages of their growth cycle. We will examine different examples on wheat of high-throughput phenotyping methodologies at various plant phenological stages. This includes (i) the automated counting of agronomic yield components, such as seedling or spike density; (ii) the assessment of physiological responses in terms of radiation use efficiency or water status; and (iii) the integration of this phenotypic information into empirical yield prediction and stability models. Emphasis will be placed on affordable approaches for plant phenotyping.